Most business organizations today rely on computing power for their business services, including data analysis, supply chain management, inventory tracking, online transactions and customer support. This computing power comes in the form of Web services, Web portals and other open source and proprietary applications hosted in either leased or owned data centers. These data centers use a significant amount of electrical power, both directly through data center computational appliances and indirectly through associated humidity and thermal conditioners. Recent data shows that almost 50% of power delivered to a server farm is spent on cooling infrastructure, while less than 50% is actually utilized in server consumption. The amount of electrical power use during the computational activity inside the server translates into a thermal load. The amount of electric power spent to maintain the operational temperature is also dependent on server air flow characteristics, the relative location of the server hardware within the rack, and other parameters as described later in this disclosure. Even though there is a direct relationship between the computational power utilized by the data centers and supplied electric power, the factors affecting that relationship are many, and the instrumentation and analysis needed to quantify them to the required precision for effective control is challenging. Existing power control mechanisms do not attempt to correlate such utilization with given electrical supply units, and hence fall short of global optimization of the power utilization in data centers and server installations. The above-disclosed related application describes a systematic procedure and apparatus to achieve such monitoring and control using collaborative server computational power measurement and electrical power units consumed under different environmental operational conditions. This related application describes a method to provide the necessary adaptive learning required to address diverse data center server farms and their infrastructure installations. The heuristics used in the method take into account the server hardware, and their associated requirements and locations inside server rack remote locations within the data centers.
While a number of methods are described in the above related application, a class of techniques used in low-level power management but which have not been applied in monitoring and controlling the direct relationship between the computational power utilized by the data centers and supplied electric power are described in the present application. Specifically, techniques for controlling the amount of energy consumption at the motherboard and CPU level may also be used in the monitoring and control of power utilized by servers in data centers
Heretofore, such capabilities have been applied in a piecemeal manner primarily to power management in laptop and other mobile systems. There remains a need in the art to apply this technique to systematic way such as described in the above cross-referenced application, to the balance of the computational power utilized by the data centers versus supplied electric power.